“Which version is better?”
Compare changes against the same examples and criteria, not whichever output looks best today.
AI evaluation and engineering firm
Know where
your AI breaks.
We design and run evaluations for AI applications, models, and agents. Find the failures, test the next change, and give your team a quality bar it can actually use.
- 10 business days
- First engagement. One use case, a repeatable evaluation, a clear next step.
- 3 engagement paths
- Reliability Sprint, Production AI Pilot, and Engineering Pod.
- Your stack
- Existing tools where they fit. Artifacts your team keeps.
EVALUATION NOTE / 001
Rubrex internal systemA fluent answer isn’t
A fluent answer isn’t
a reliable answer.
Testing an AI-generated evaluation rubric.
06Test profiles
03Critic lenses
Observed failureFinding
01Generic scoring anchorsRevise
02Overlapping criteriaRevise
03Unsupported specificityRevise
7
prompt fixesfrom the evaluation findings
Deterministic checks + human release gate.
Semantic LLM judge: planned, not live.
EVERY ENGAGEMENTBuilt for teams shipping AI applications, models, and agentic workflows.
- Written scope and quoteAgreed before any work begins.
- Agreed data termsAccess, tools, and retention settled up front.
- Inspectable resultsAgreed criteria, a readout, and a documented handoff.
- A senior teamClose to the work and accountable for it.
01The problem
A better demo isn’t
proof of a better system.
If your team can’t explain what changed, a new prompt or model is another guess.
“Why did our agent fail?”
Separate bad answers from retrieval misses, tool errors, and incomplete tasks. Know what to fix first.
“Can we release this?”
Turn real failures into regression cases. Make the quality bar explicit before the next release.
02Start here
Ten days.
A quality bar you can use.
The Rubrex AI Reliability Sprint. One priority use case, a repeatable evaluation, and a clear next step.
One use case.10 business days · A focused evaluation
For AI product teams and engineering leaders with a working system, real examples, and a quality problem worth solving.
Scope your sprintScope and pricing agreed before kickoff. Starts once access and inputs are ready.
What we need from you
One technical owner, access to the system and representative data, two working sessions, and timely feedback.
THE WORK, AND WHAT YOU KEEP
- 01
Define the quality bar
Success criteria, an anchored rubric, and a representative evaluation set.
- 02
Establish the baseline
A repeatable procedure or lightweight harness, plus the first evaluation run.
- 03
Find and test a fix
A failure taxonomy, one targeted improvement experiment, and a rerun.
- 04
Make the next decision
Your evaluation artifacts, an executive readout, and a prioritized 30-day plan.
A FOCUSED SCOPE
Not a full product build, unlimited labeling, ongoing monitoring, or a comprehensive security audit. Additional use cases and implementation are scoped separately.
03Selected work
The method, in practice.
Work we can explain and stand behind.
Internal systems and products built by our team, clearly labeled.
01 / Rubrex internal case study
Evaluating the
evaluation generator.
A generated rubric can sound credible and still be impossible to score. We built a quality contract, tested representative cases, and turned the failures into prompt changes.
Read the case study6profiles×3critic lenses→7prompt fixes
✓ Structural validation on every run
✓ Human review before release
○ Semantic judge remains planned
02 / AI product built by the Rubrex team
ResumizeAI
An AI job-application workflow spanning structured generation, editing, document delivery, agent tools, and evaluation gates.
Built end to end, from user workflow to release process.
Request a product walkthrough04Beyond evaluation
Need the system
built, too?
Our senior engineering team builds agentic workflows and production software. A scoped pilot or an embedded pod takes ownership of the work, from implementation to handoff.
Explore engineering engagements Scoped production pilots · Ongoing engineering pods05Who you work with
Close to the work.
Accountable for it.
Rubrex is a specialist AI evaluation and engineering firm. A senior team scopes the work, runs the evaluation, hands over the artifacts, and stands behind them.
Our team’s experience includes AI training and evaluation, AI product development, and the infrastructure that runs those products.
We bring that evaluation discipline into your actual product: the examples your customers generate, the failures your team sees, and the next release you need to make.
Every engagement starts with a written scope and quote. While the work runs, you get working sessions and a readout, and it ends with a documented handoff. Your team can carry the work forward without us.
Your stackWork with existing tools where they fit.
Your evidenceAgreed criteria, inspectable results.
Your next stepUseful work, even without a follow-on.
06A few useful answers
Before we talk.
Do we need to train our own model?
No. The sprint can evaluate applications built on model APIs, retrieval systems, fine-tuned models, or tool-using agents. We start with a working use case and the behavior you need to measure.
We already use an evaluation platform. Can you work with it?
Yes. A platform can run experiments and collect traces; your team still needs useful examples, scoring criteria, calibrated judgment, and someone to investigate failures. We can do that work in your existing stack where it fits.
What if we already have a rubric and need evaluations run?
We can scope managed execution against an existing rubric, including output grading or preference comparisons where appropriate. Volume, review requirements, cadence, and pricing are agreed separately.
Will the sprint make our system production-ready?
The sprint establishes a baseline, tests one improvement, and leaves a repeatable evaluation and plan. It is not a guarantee of production readiness or a full security assessment. Implementation can follow through a separately scoped pilot.
How do you handle our data?
Before work starts, we agree what data is needed, who can access it, which tools may process it, and the retention and deletion terms. Do not send sensitive datasets, credentials, or customer records through this contact form.
Can we start with engineering instead?
Yes, when the workstream and acceptance criteria are clear. A Production AI Pilot covers one defined workflow; an embedded engineering pod is for ongoing delivery. You do not have to buy an evaluation sprint first.
07Let’s talk
What isn’t working
the way it should?
Tell us what you’re building, where it breaks, and what you need next. We’ll reply by email to discuss fit and scope.
evals@rubrex.aiWHAT HAPPENS NEXT
- A short email exchange about the problem.
- A technical conversation if there’s a fit.
- A written scope and quote before any work begins.